import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

class ClusterUtils(object):
    def __init__(self):
        self.df = pd.read_csv('./scenic_data.csv')

    def get_cluster(self):
        features = self.df[['non_weekend', 'out_province_ratio', 'elderly_ratio']]  # Fixed bracket syntax
        scaler = StandardScaler()
        
        scaler_features = scaler.fit_transform(features)

        kmeans = KMeans(n_clusters=4, random_state=42)
        self.df['cluster'] = kmeans.fit_predict(scaler_features)

        # 查看聚类结果
        print(self.df[['tourist_agency_name', 'cluster']])
        
        # 查看聚类中心
        centers = scaler.inverse_transform(kmeans.cluster_centers_)
        print(pd.DataFrame(centers, columns=['non_weekend', 'out_province_ratio', 'elderly_ratio']))
        
        # 保存结果
        self.df.to_csv('clustered_agencies.csv', index=False)

        # 将聚类中心转换为DataFrame
        centers_df = pd.DataFrame(centers, columns=features.columns)
        centers_df['cluster'] = [f'Cluster {i}' for i in range(centers.shape[0])]

        # 将宽表转换为长表（适合seaborn）
        centers_long = centers_df.melt(id_vars='cluster', var_name='feature', value_name='value')    
        # 绘制分组柱状图
        colors = ['#1f77b4', '#ff7f0e', '#2ca02c']  # 蓝,橙,绿
        plt.figure(figsize=(16, 6))
        sns.barplot(x='feature', y='value', hue='cluster', data=centers_long, palette=colors)

        # 设置字体
        plt.rcParams['font.sans-serif'] = ['SimHei']  
        plt.rcParams['axes.unicode_minus'] = False
        plt.title('聚类中心特征对比')
        plt.ylabel('比例')
        plt.ylim(0, 1)
        plt.legend(title='簇', bbox_to_anchor=(1.05, 1), loc='upper left')

        # 添加数值标签
        for p in plt.gca().patches:
            height = p.get_height()
            plt.gca().text(p.get_x() + p.get_width() / 2, height + 0.02,
                        f'{height:.2f}', ha='center') 
        plt.tight_layout()  
        plt.show()
if __name__ == '__main__':
    ci = ClusterUtils()
    ci.get_cluster()